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Computer Vision
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Face Recognition Quiz Questions
1.
What is the primary goal of face recognition in computer vision?
A. Image resizing
B. Noise reduction
C. Identifying and verifying individuals based on facial features
D. Color correction
view answer:
C. Identifying and verifying individuals based on facial features
Explanation:
Face recognition aims to identify and verify individuals based on their facial features.
2.
Which type of face recognition system is designed to compare an input face to a database of known faces to find a match?
A. Verification
B. Identification
C. Liveness detection
D. Feature extraction
view answer:
B. Identification
Explanation:
Identification face recognition systems compare an input face to a database of known faces to find a match.
3.
What is the primary advantage of using 3D face recognition over 2D face recognition?
A. Noise reduction
B. Improved image resizing
C. Resistance to spoofing with 2D photos
D. Color correction
view answer:
C. Resistance to spoofing with 2D photos
Explanation:
3D face recognition is more resistant to spoofing with 2D photos compared to 2D face recognition.
4.
Which component of a face recognition system is responsible for capturing an individual's facial features from an image or video stream?
A. Face detection
B. Face alignment
C. Feature extraction
D. Face verification
view answer:
C. Feature extraction
Explanation:
Feature extraction is responsible for capturing an individual's facial features from an image or video stream in face recognition.
5.
Which biometric feature is commonly used for face recognition?
A. Fingerprint
B. Iris
C. Voice
D. Palmprint
view answer:
B. Iris
Explanation:
The iris is a common biometric feature used for face recognition.
6.
What is the primary purpose of "liveness detection" in face recognition?
A. Image resizing
B. Color correction
C. Determining whether the detected face is from a live person or a photograph
D. Noise reduction
view answer:
C. Determining whether the detected face is from a live person or a photograph
Explanation:
Liveness detection in face recognition determines whether the detected face is from a live person or a photograph.
7.
In face recognition, what is the main advantage of using "deep learning-based" models?
A. Precise image resizing
B. Real-time performance
C. Ability to learn and represent complex facial features
D. Superior noise reduction
view answer:
C. Ability to learn and represent complex facial features
Explanation:
Deep learning-based models can learn and represent complex facial features, making them effective in face recognition.
8.
What is the primary challenge in face recognition when dealing with variations in lighting conditions?
A. Color correction
B. Image resizing
C. Noise reduction
D. Illumination invariance
view answer:
D. Illumination invariance
Explanation:
The primary challenge in face recognition with lighting variations is achieving illumination invariance.
9.
Which face recognition technique is based on mapping facial landmarks and extracting geometric features?
A. Histogram equalization
B. Eigenface method
C. Landmark-based recognition
D. Median filtering
view answer:
C. Landmark-based recognition
Explanation:
Landmark-based recognition maps facial landmarks and extracts geometric features for recognition.
10.
What is the primary purpose of "face encoding" in face recognition?
A. Image resizing
B. Extracting and representing facial features as a numerical code
C. Noise reduction
D. Color correction
view answer:
B. Extracting and representing facial features as a numerical code
Explanation:
Face encoding involves extracting and representing facial features as a numerical code for face recognition.
11.
In face recognition, what is the primary role of the "verification" process?
A. Image resizing
B. Noise reduction
C. Comparing an individual's face to their stored template to verify their identity
D. Color correction
view answer:
C. Comparing an individual's face to their stored template to verify their identity
Explanation:
Verification in face recognition involves comparing an individual's face to their stored template to verify their identity.
12.
Which face recognition approach is focused on detecting the presence of faces in an image or video stream?
A. Feature extraction
B. Face detection
C. Liveness detection
D. Histogram equalization
view answer:
B. Face detection
Explanation:
Face detection is focused on detecting the presence of faces in an image or video stream.
13.
What is the primary challenge in face recognition when dealing with changes in facial expressions?
A. Image resizing
B. Noise reduction
C. Expression invariance
D. Color correction
view answer:
C. Expression invariance
Explanation:
The primary challenge in face recognition with changes in facial expressions is achieving expression invariance.
14.
Which face recognition method is based on the idea of representing faces as linear combinations of "eigenfaces"?
A. Histogram equalization
B. Eigenface method
C. Median filtering
D. Template matching
view answer:
B. Eigenface method
Explanation:
The Eigenface method represents faces as linear combinations of "eigenfaces."
15.
What is the primary purpose of "face normalization" in face recognition?
A. Image resizing
B. Noise reduction
C. Bringing faces to a common format to reduce variations
D. Color correction
view answer:
C. Bringing faces to a common format to reduce variations
Explanation:
Face normalization brings faces to a common format to reduce variations in face recognition.
16.
Which facial feature is often used for gender classification in face recognition?
A. Nose shape
B. Eye color
C. Lip thickness
D. Jawline
view answer:
D. Jawline
Explanation:
The jawline is often used for gender classification in face recognition.
17.
In face recognition, what is the primary goal of "face matching"?
A. Noise reduction
B. Color correction
C. Identifying the degree of facial similarity between two faces
D. Image resizing
view answer:
C. Identifying the degree of facial similarity between two faces
Explanation:
Face matching in face recognition identifies the degree of facial similarity between two faces.
18.
What is the primary role of "anti-spoofing" techniques in face recognition?
A. Noise reduction
B. Image resizing
C. Detecting and preventing fraudulent attempts to fool the system using photos or videos
D. Color correction
view answer:
C. Detecting and preventing fraudulent attempts to fool the system using photos or videos
Explanation:
Anti-spoofing techniques in face recognition detect and prevent fraudulent attempts to fool the system using photos or videos.
19.
Which face recognition approach is suitable for identifying individuals from a group of known individuals?
A. Liveness detection
B. Face verification
C. Face identification
D. Feature extraction
view answer:
C. Face identification
Explanation:
Face identification in face recognition is used to identify individuals from a group of known individuals.
20.
What is the primary purpose of "face clustering" in face recognition?
A. Image resizing
B. Noise reduction
C. Grouping similar faces together based on facial features
D. Color correction
view answer:
C. Grouping similar faces together based on facial features
Explanation:
Face clustering groups similar faces together based on facial features in face recognition.
21.
Which facial feature is often used for age estimation in face recognition?
A. Earlobe shape
B. Eye wrinkles
C. Forehead size
D. Lip thickness
view answer:
B. Eye wrinkles
Explanation:
Eye wrinkles are often used for age estimation in face recognition.
22.
What is the primary advantage of using "deep learning-based" face recognition models over traditional methods?
A. Noise reduction
B. Real-time performance
C. Ability to learn high-level features directly from data
D. Image resizing capabilities
view answer:
C. Ability to learn high-level features directly from data
Explanation:
Deep learning-based face recognition models can learn high-level features directly from data, improving accuracy.
23.
What is the primary goal of "face anti-spoofing" in face recognition?
A. Image resizing
B. Noise reduction
C. Detecting and preventing fraudulent attempts to impersonate someone's face using fake images or videos
D. Color correction
view answer:
C. Detecting and preventing fraudulent attempts to impersonate someone's face using fake images or videos
Explanation:
Face anti-spoofing aims to detect and prevent fraudulent attempts to impersonate someone's face using fake images or videos.
24.
In face recognition, what does "one-shot learning" refer to?
A. Noise reduction
B. Learning to recognize a person's face with just one example
C. Image resizing
D. Color correction
view answer:
B. Learning to recognize a person's face with just one example
Explanation:
One-shot learning in face recognition involves learning to recognize a person's face with just one example.
25.
What is the primary challenge in "cross-modal" face recognition?
A. Image resizing
B. Noise reduction
C. Matching faces from different sensory modalities, such as photos and sketches
D. Color correction
view answer:
C. Matching faces from different sensory modalities, such as photos and sketches
Explanation:
The primary challenge in cross-modal face recognition is matching faces from different sensory modalities, such as photos and sketches.
26.
Which face recognition method is based on the idea of representing faces as points in a high-dimensional space and finding similarity using distances between points?
A. Histogram equalization
B. Eigenface method
C. Face embedding
D. Median filtering
view answer:
C. Face embedding
Explanation:
Face embedding represents faces as points in a high-dimensional space and finds similarity using distances between points.
27.
What is the primary purpose of "deep face recognition" models in face recognition?
A. Image resizing
B. Noise reduction
C. Learning powerful feature representations for face recognition
D. Color correction
view answer:
C. Learning powerful feature representations for face recognition
Explanation:
Deep face recognition models are designed to learn powerful feature representations for face recognition.
28.
What is the primary challenge in face recognition when dealing with variations in pose and viewing angles?
A. Color correction
B. Image resizing
C. Pose invariance
D. Noise reduction
view answer:
C. Pose invariance
Explanation:
The primary challenge in face recognition with variations in pose and viewing angles is achieving pose invariance.
29.
Which face recognition method is known for its ability to recognize faces even when they are partially occluded?
A. Template matching
B. Median filtering
C. Eigenface method
D. Histogram equalization
view answer:
A. Template matching
Explanation:
Template matching is known for its ability to recognize faces even when they are partially occluded.
30.
What is the primary advantage of using "ensemble" methods in face recognition?
A. Improved image resizing capabilities
B. Noise reduction
C. Enhanced accuracy and robustness through the combination of multiple models
D. Precise color correction
view answer:
C. Enhanced accuracy and robustness through the combination of multiple models
Explanation:
Ensemble methods in face recognition enhance accuracy and robustness by combining multiple models.
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